Accurately determining demand forecasts for products are paramount concerns for retail organizations. Demand forecasts are used for inventory control, purchase planning, work force planning, and other planning needs of organizations. Inaccurate demand forecasts can result in shortages of inventory that are needed to meet current demand, which can result in lost sales and revenues for the organizations. Conversely, inventory that exceeds a current demand can adversely impact the profits of an organization. Excessive inventory of perishable goods may lead to a loss for those goods.
Inferior forecasting science and gut feel decisions on inventory have created significant stock-out conditions across the industry. Recent studies quantify stock-outs in the retail industry at 5 to 8%, while overstock conditions caused by poor forecasts and buys continue to climb.
This challenge makes accurate consumer demand forecasting and automated replenishment techniques more necessary than ever. A highly accurate forecast not only removes the guess work for the real potential of both products and stores/distribution centers, but delivers improved customer satisfaction, increased sales, improved inventory turns and significant return on investment.
Teradata, a division of NCR Corporation, has developed a suite of analytical applications for the retail business, referred to as Teradata Demand Chain Management, that provides retailers with the tools they need for product demand forecasting, planning and replenishment. As illustrated in FIG. 1, the Teradata Demand Chain Management analytical application suite 101 is shown to be part of a data warehouse solution for the retail industries built upon NCR Corporation's Teradata Data Warehouse 103, using a Teradata Retail Logical Data Model (RLDM) 105. The key modules contained within the Teradata Demand Chain Management application suite 103, organized into forecasting and planning applications 107 and replenishment applications 109, are:
Demand Forecasting: The Demand Forecasting module 111 provides store/SKU (Stock Keeping Unit) level forecasting that responds to unique local customer demand. It continually compares historical and current demand and utilizes several methods to determine the best product demand forecast.
Seasonal Profile The Seasonal Profile module 113 automatically calculates seasonal selling patterns at all levels of merchandise and location, using detailed historical sales data.
Contribution: Contribution module 117 provides an automatic categorization of SKUs, merchandise categories and locations by contribution codes. These codes are used by the replenishment system to ensure the service levels, replenishment rules and space allocation are constantly favoring those items preferred by the customer.
Promotions Management: The Promotions Management module 119 automatically calculates the precise additional stock needed to meet demand resulting from promotional activity.
Automated Replenishment: Automated Replenishment module 121 provides suggested order quantities based on business policies, service levels, forecast error, risk stock, review times, and lead times.
Allocation: The Allocation module 123 determines distribution of products from the warehouse to the store.
The Teradata Demand Chain Management solution described above provides a retailer with improved customer service levels and reductions in unproductive inventory, and is particularly adept at assisting a retailer forecast and plan for seasonal sales cycles. However, for many retailers the sales pattern of different products varies from day to day. Some products sell the same throughout a week while the sale of some products follows a certain pattern that, for example, might have higher sales over the weekend as compared to during the weekend. Holidays also affect the sales pattern for certain products. Before a long holiday, sales may be higher for some products, e.g., perishable goods, milk, soft drinks and other highly consumable items, because stores may be closed or shopping inconvenient for consumers. Most retailers and particularly Food and Grocery retailers need to accurately forecast daily sales in order to minimize store inventories and optimize store replenishment schedules.
Therefore, there exists a need for improved demand chain forecasting tools that provide retailers with an accurate picture of product sales patterns over a week. Additionally, such forecasting tools may assist a retailer in forecasting product sales during holiday periods or store closures, and in implementing sales for promotions that are shorter than a week in duration.